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Some of the applications to materials evaluation and property determination of thermographic NDE using digital postprocessing of sequences of thermograms are demonstrated. A generic description is given of the steps used in postprocessing for obtaining material property values.
Abstract Polypropylene (PP) is one of the most widely used plastics, yet its recycling remains limited, with less than 1% of solid waste PP being reprocessed. Mechanical recycling through extrusion is the most practical method, but inconsistent reprocessing conditions introduce variability in material properties. While temperature, screw speed, and residence time influence the thermomechanical stress applied during reprocessing, there are no standardized guidelines for optimizing these parameters. This study examines how these factors shape the properties of recycled PP, using conditions designed to mimic post‐industrial recycled (PIR) scrap. Residence time was measured using colorimetric tracking and correlated with molecular weight, viscosity, and mechanical properties over multiple extrusion cycles. Data‐driven modeling, including response surface methodology, support vector machines, and artificial neural networks, identified processing temperature as the dominant factor in material degradation, followed by residence time. Mechanical properties remained stable, while viscosity decreased predictably with increasing residence time. By linking reprocessing conditions to property evolution, this study provides a method to optimize processing parameters and reduce variability in recycled PP. These findings help manufacturers improve process control, making recycled PP more predictable for reuse in manufacturing. Highlights Study of PIR‐quality PP without additives or compatibilizers. Residence time analysis shows processing temperature drives PP property changes. Mark‐Houwink enables quick molecular weight checks for quality control. Models predict mechanical and rheological shifts in reprocessing. Optimized processing parameters minimize property degradation in recycling.
Upcoming projects requiring high-Q ~650 MHz medium-to-high-! elliptical cavities drive a need to understand magnetic RF loss mechanisms and mitigations in greater detail. We explore the fastcooldown method, and high-temperature annealing (900°C) in 644- 650 MHz SRF cavities fabricated from two different niobium vendors: Tokyo-Denkai (TD), and Ningxia (NX). Using EBSD and PPMS methods, we aim to trace cavity flux expulsion efficiency to specific, measurable properties of the bulk niobium material, which, if identified, can lead to methods by which the flux expulsion properties of Nb material can be predicted prior to cavity fabrication.
As a highly abundant renewable carbon source, lignin can be converted to a variety of advanced carbon materials with tailorable properties through slow pyrolysis. In this study, slow pyrolysis of kraft lignin, for the first time, was investigated with a commercial pyrolysis–gas chromatography–mass spectrometry (Py–GC–MS) system through evolved gas analysis-MS (EGA-MS) and heart-cutting-GC–MS (HC-GC–MS) analyses. These analyses allow recovery and examination of the multiphased gas products generated from thermal decomposition of lignin during slow pyrolysis at a controlled heating rate over a long time course, thus making it possible to link operation conditions, pyrolysis chemistry, and carbon material properties. The overall product distributions, including volatiles and solid products, were quantitatively tracked at different heating rates (2, 20, and 40 °C/min) and different temperature regions (100–200, 200–300, and 300–600 °C). Solid residues were further characterized using a suite of analytical tools, in correlation with the investigation of formation mechanisms of volatiles to reveal the reaction chemistry of lignin during slow pyrolysis and to determine the morphology, pore structure, and interfacial chemical properties. This study provides critical insights into the slow pyrolysis chemistry of lignin and the properties of the resulting carbon material. These results will facilitate a better design and control of the lignin slow pyrolysis process for synthesizing functional carbon materials.
The resistance of elastomeric materials to high-pressure hydrogen-induced damage is essential for ensuring the reliability of hydrogen infrastructure. Here, in this study, we systematically investigated the swelling behavior and hydrogen transport properties of four elastomer types – EPDM, NBR, FKM, and HNBR – using a custom in-situ view cell system capable of real-time monitoring during decompression from pressures up to 96.5 MPa. Each elastomer was formulated with and without fillers and plasticizers to assess the effects of formulation on swelling response. Thermal desorption analysis (TDA) was employed to determine equilibrium hydrogen content and diffusion coefficients, providing insight into gas uptake and mobility within each material. Correlation analyses using Pearson and Spearman coefficients revealed that the diffusion coefficient showed a stronger relationship with swelling behavior than hydrogen content, highlighting the dominant role of hydrogen mobility. Filled elastomers, particularly those with carbon black, consistently showed reduced swelling due to enhanced stiffness and reduced diffusivity. These results deepen our understanding of diffuso-mechanical interactions in elastomers and support the rational design of sealing materials for high-pressure hydrogen systems.
The influence of strength, fracture toughness, and crack growth resistance on the design stress level has been determined from a mathematical model of crack growth and failure. The results show that to select materials properly, the operating parameters of desired life and initial flaw size must be considered simultaneously with the material properties. To do this, a method for constructing an operating surface is derived, discussed, and illustrated.
Numerical simulation of the HgCdTe growth by the vertical Bridgman method was performed using FIDAP finite element code. Double-diffusive melt convection is analyzed, as the primary factor at controls inhomogeneity of the solidified material. Temperature and concentration fields in the model are also coupled via material properties, such as thermal and solutal expansion coefficients with the dependence on both temperature and concentration, and melting temperature evaluation from pseudobinary CdTe-HgTe phase diagram. Experimental measurements were used to obtain temperature boundary conditions. Parametric study of the melt convection dependence on the gravity conditions was undertaken. It was found, that the maximum convection velocity in the melt can be reduced under certain conditions. Optimal conditions to obtain a near flat solidified interface are discussed. The predicted interface shape is in agreement with one obtained experimentally by quenching. The results of 3-D calculations are compared with previous 2- D findings. A video film featuring 3-D melt convection will be presented.
Mechanically interlocked polymers (MIPs), such as polyrotaxanes and polycatenanes, are polymer architectures that incorporate a mechanical bond. In a polyrotaxane, the mechanical bond is the result of a linear dumbbell component threaded through a ring, while in a polycatenane, it is the consequence of interlocked ring components. Furthermore, the interlocked nature of these architectures can result in high degrees of conformational freedom and mobility of their components, which can give rise to unique property profiles. In recent years the synthesis and studies of a range of MIPs has allowed researchers to build an initial understanding of how incorporating mechanical bonds within a polymer structure impacts its material properties. This review focuses on the understanding of these structure-property relationships with an outlook toward their applications, specifically focusing on four main classes of MIPs: polyrotaxanes, slide-ring gels, daisy-chain polymers, and polycatenanes.
The prospect of long-term manned space flight brings fresh urgency to the development of an integrated and fundamental approach to the study of material flammability. Currently, NASA uses two tests, the upward flame propagation test and heat and visible smoke release rate test, to assess the flammability properties of materials to be used in space under microgravity conditions. The upward flame propagation test can be considered in the context of the 2-D analysis of Emmons. This solution incorporates material properties by a "mass transfer number", B in the boundary conditions.
We present a novel deep learning (DL) approach to produce highly accurate predictions of macroscopic physical properties of solid solution binary alloys and magnetic systems. The major idea is to make use of the correlations between different physical properties in alloy systems to improve the prediction accuracy of neural network (NN) models. We use multitasking NN models to simultaneously predict the total energy, charge density and magnetic moment. These physical properties mutually serve as constraints during the training of the multitasking NN, resulting in more reliable DL models because multiple physics properties are correctly learned by a single model. Two binary alloys, copper–gold (CuAu) and iron–platinum (FePt), were studied. Our results show that once the multitasking NN's are trained, they can estimate the material properties for a specific configuration hundreds of times faster than first-principles density functional theory calculations while retaining comparable accuracy. We used a simple measure based on the root-mean-squared errors to quantify the quality of the NN models, and found that the inclusion of charge density and magnetic moment as physical constraints leads to more stable models that exhibit improved accuracy and reduced uncertainty for the energy predictions.
Establishing a data-driven pipeline for the discovery of novel materials requires the engineering of material features that can be feasibly calculated and can be applied to predict a material’s target properties. Here we propose a new class of descriptors for describing crystal structures, which we term Robust One-Shot Ab initio (ROSA) descriptors. ROSA is computationally cheap and is shown to accurately predict a range of material properties. These simple and intuitive class of descriptors are generated from the energetics of a material at a low level of theory using an incomplete ab initio calculation. We demonstrate how the incorporation of ROSA descriptors in ML-based property prediction leads to accurate predictions over a wide range of crystals, amorphized crystals, metal–organic frameworks and molecules. We believe that the low computational cost and ease of use of these descriptors will significantly improve ML-based predictions.
Device-quality a-Si:H and a-SiC:H films have been deposited using electron cyclotron resonance (ECR) microwave plasmas of SiH4, CH4, and H2 mixtures. Typical material properties of ECR-deposited, photosensitive a-Si:H films are: (1) high photosensitivity up to 2 x 106 with a photoconductivity of 10 exp -5 to 10 exp -4/(Ohm-cm), (2) a Tauc gap of 1.75 to 1.85 eV, (3) an Urbach slope of 50-60 meV determined by the constant photocurrent method, and (4) an integrated defect density of 1-2 x 10 exp 16/cu cm determined by junction capacitance measurements. Highly conductive, p-type a-SiC:H films have been produced by ECR plasmas with a conductivity of 0.2/(Ohm-cm).
Foams are classified as either rigid or flexible and are commonly used in applications such as pipe insulation, carpet fibers, and chair foam cushions. Polyurethane foams are excellent thermal insulators, have excellent mechanical properties, and are chemically inert. With the addition of fire protective additives, polyurethane foams have the ability to intumescence when exposed to high temperatures. For the future storage of the 9977 nuclear shipping packages, the material properties of the General Plastics 3716 rigid polyurethane foam were evaluated for long term performance by comparing aged samples to unaged samples. General Plastics 3716 rigid polyurethane foam was aged from November 2014 to June 2019 at varying temperatures of: 160 deg. F, 160 deg. F + 50% relative humidity (RH), 185 deg. F, 215 deg. F, and 250 deg. F. The foam was then analyzed via Fourier Transform Infrared Spectroscopy (FTIR), 3D Optical Microscope, Thermal Gravimetric Analysis (TGA), Differential Scanning Calorimetry (DSC). FTIR and Optical Microscope: No significant change to chemical or physical structure was evident by FTIR or optical microscope analysis. DSC: Decrease of heat capacity over time for all oven temperatures except a slight increase for the 185 deg. F conditioned foams. Largest decrease evident with foams conditioned at 250 deg. F. A slight increase in heat capacity for humid samples. Similar trends in heat capacities at 71 deg. C and 102 deg. C. TGA: As conditioning temperature increased, a decline was evident in the average percent weight loss, onset temperature, and first derivative peak temperature. No significant effect due to humidity except for a decrease in first derivative peak temperature. Unaged samples exhibited strong intumescence. Foams conditioned at 160 deg. F exhibited slight intumescence. Results suggest fire protective additives decomposed at higher temperatures, which contributed to a lower average percent mass loss in higher temperature conditioning. Further thermal degradation testing would be beneficial to evaluate the ability of the conditioned foams to intumesce. Evaluate irradiation dose rate effects on conditioned foam. Possible conditioning in an environment with a known humidity or analyzing a new aliquot of sample for each trial to achieve a more representative heat capacity calculation.
Energy storage using lithium-ion cells dominates consumer electronics and is rapidly becoming predominant in electric vehicles and grid-scale energy storage, but the high energy densities attained lead to the potential for release of this stored chemical energy. This article introduces some of the paths by which this energy might be unintentionally released, relating cell material properties to the physical processes associated with this potential release. The selected paths focus on the anode–electrolyte and cathode–electrolyte interactions that are of typical concern for current and near-future systems. Relevant material processes include bulk phase transformations, bulk diffusion, surface reactions, transport limitations across insulating passivation layers, and the potential for more complex material structures to enhance safety. We also discuss the development, parameterization, and application of predictive models for this energy release and give examples of the application of these models to gain further insight into the development of safer energy storage systems.
Machine learning-based data-driven modeling can allow computationally efficient time-dependent solutions of PDEs, such as those that describe subsurface multiphysical problems. In this work, our previous approach (Kadeethum et al., 2021d) of conditional generative adversarial networks (cGAN) developed for the solution of steady-state problems involving highly heterogeneous material properties is extended to time-dependent problems by adopting the concept of continuous cGAN (CcGAN). The CcGAN that can condition continuous variables is developed to incorporate the time domain through either element-wise addition or conditional batch normalization. Moreover, this framework can handle training data that contain different timestamps and then predict timestamps that do not exist in the training data. As a numerical example, the transient response of the coupled poroelastic process is studied in two different permeability fields: Zinn & Harvey transformation and a bimodal transformation. The proposed CcGAN uses heterogeneous permeability fields as input parameters while pressure and displacement fields over time are model output. Our results show that the model provides sufficient accuracy with computational speed-up. This robust framework will enable us to perform real-time reservoir management and robust uncertainty quantification in poroelastic problems.
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Abstract A recently developed model of the cold cap—the reacting glass batch (melter feeds) floating on molten glass in an electric glass melter—couples heat transfer with the feed‐to‐glass conversion kinetics. The model allows for determining the distributions of temperature and various properties within the cold cap. In the present study, this model is applied to four melter feeds designed for high‐level and low‐activity nuclear wastes. Profiles of temperature, conversion degree, cold cap porosity and density, condensed matter velocity, and heating rate were determined using the material properties of the cold cap. Effects of vigorous foaming at the cold cap bottom were considered. Density, thermal conductivity, and glass production rate strongly affect the cold cap thickness and the fraction of undissolved silica entering the melt under the cold cap. The heating rate profile in the cold cap is highly nonlinear, with high heating rates observed in the foam layer.
A static and dynamic finite element analysis is conducted on a U.S. Army OH-58 composite tail boom and compared with test data. The tail boom is a filament-wound graphite/epoxy monocoque structure. The structural design of the composite tail boom skin is based on 50-percent graphite fiber volume. However, material tests on representative samples of the tail boom skin reveal that the graphite fiber-volume fraction varied from 44.6 to 49.3 percent. To determine the effect of using measured material properties, static and dynamic finite element analyses are conducted for three fiber-volume conditions of 45, 48, and 50 percent. The static and dynamic model with the 45-percent fiber-volume graphite skins gives the closest agreement with test data.